Semiotic Aspects of Differential Equations: Analytical and Graphical Competency in the USA and Tunisia

Covid-19 Prediction in USA using modified SIR derived model

10.1101/2020.12.20.20248600 ◽

2020 ◽

Author(s):

Jathin desan

Keyword(s):

Markov Chain ◽

Performance Analysis ◽

Differential Equations ◽

Sir Model ◽

Disease Dynamics ◽

Analytical Results ◽

Proposed Model ◽

The Usa ◽

Death Cases ◽

Discrete Markov Chain

AbstractThe Covid-19 pandemic is rapidly extended into the extraordinary crisis. Based on the SIR model and published datasets the Covid-19 spread is assessed and predicted in USA in terms of susceptible, recovered and infected in the communities is focused on this study. For modelling the USA pandemic prediction several variants have been utilized. The SIR model splits the whole population into three components such as Susceptible (S), Infected (I) and Recovered or Removed (R). A collection of differential equations have been utilized to propagate the model and resolve the disease dynamics. In the proposed study, the prediction of covid-19 based on time is performed using the modified SIR derived model SIR-D with discrete markov chain. This proposed technique analyse and forecasting the covid-19 spread in 19 states of USA. The performance analysis of the proposed Analytical results revealed that though the probable uncertainty of the proposed model provides prediction, it becomes difficult to determine the death cases in future.

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Analyzing the effect of relaxing restriction on the COVID-19 outbreak for some US states

10.1101/2021.04.19.21255759 ◽

2021 ◽

Author(s):

Mahir Demir ◽

ibrahim Halil Aslan ◽

Suzanne Lenhart

Keyword(s):

Differential Equations ◽

Movement Restriction ◽

Current Management ◽

Us States ◽

Social Distancing ◽

Transmission Rates ◽

Disease Propagation ◽

Parameter Estimations ◽

The Usa ◽

Using Data

The ongoing pandemic disease COVID-19 has caused worldwide social and financial disrup- tion. As many countries are engaged in designing vaccines, the harmful second and third waves of COVID-19 have already appeared in many countries. To investigate changes in transmission rates and the effect of social distancing in the USA, we formulate a system of ordinary differential equations using data of confirmed cases and deaths in these states: California, Texas, Florida, Georgia, Illinois, Louisiana, Michigan, and Missouri in the USA to be able to investigate changes in transmission rates of the outbreak and effect of social distancing. Our models and the corre- sponding parameter estimations show social distancing reduces the transmission by 60% to 90%, and thus obeying the movement restriction rules plays a crucial rule to reduce the magnitudes of the outbreak waves. Our analysis shows the current management restrictions do not sufficiently slow the disease propagation.

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